from __future__ import annotations

import numpy as np
from numpy import floataa
from numpy.typing import NDArray





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					)		->	list[float]:







 '''simple docstring'''
 UpperCamelCase  ,     UpperCamelCase			  =      coefficient_matrix.shape
 UpperCamelCase  ,     UpperCamelCase			  =      constant_matrix.shape

 if rowsa != colsa:
  UpperCamelCase			  =      f"""Coefficient matrix dimensions must be nxn but received {rowsa}x{colsa}"""
  raise ValueError(UpperCamelCase_       )

 if colsa != 1:
  UpperCamelCase			  =      f"""Constant matrix must be nx1 but received {rowsa}x{colsa}"""
  raise ValueError(UpperCamelCase_       )

 if rowsa != rowsa:
  UpperCamelCase			  =      (
      """Coefficient and constant matrices dimensions must be nxn and nx1 but """
      f"""received {rowsa}x{colsa} and {rowsa}x{colsa}"""
  )
  raise ValueError(UpperCamelCase_       )

 if len(UpperCamelCase_       ) != rowsa:
  UpperCamelCase			  =      (
      """Number of initial values must be equal to number of rows in coefficient """
      f"""matrix but received {len(UpperCamelCase_       )} and {rowsa}"""
  )
  raise ValueError(UpperCamelCase_       )

 if iterations <= 0:
  raise ValueError("""Iterations must be at least 1"""       )

 UpperCamelCase			  =      np.concatenate(
     (coefficient_matrix, constant_matrix)							,					axis=1       )

 UpperCamelCase  ,     UpperCamelCase			  =      table.shape

 strictly_diagonally_dominant(UpperCamelCase_       )

 # Iterates the whole matrix for given number of times
 for _ in range(UpperCamelCase_       ):
  UpperCamelCase			  =      []
  for row in range(UpperCamelCase_       ):
   UpperCamelCase			  =      0
   for col in range(UpperCamelCase_       ):
    if col == row:
     UpperCamelCase			  =      table[row][col]
    elif col == cols - 1:
     UpperCamelCase			  =      table[row][col]
    else:
     temp += (-1) * table[row][col] * init_val[col]
   UpperCamelCase			  =      (temp + val) / denom
   new_val.append(UpperCamelCase_       )
  UpperCamelCase			  =      new_val

 return [float(UpperCamelCase_       ) for i in new_val]





def 			lowercase(	UpperCamelCase_       )		->	bool:







 '''simple docstring'''
 UpperCamelCase  ,     UpperCamelCase			  =      table.shape

 UpperCamelCase			  =      True

 for i in range(0							,					UpperCamelCase_       ):
  UpperCamelCase			  =      0
  for j in range(0							,					cols - 1       ):
   if i == j:
    continue
   else:
    total += table[i][j]

  if table[i][i] <= total:
   raise ValueError("""Coefficient matrix is not strictly diagonally dominant"""       )

 return is_diagonally_dominant


# Test Cases
if __name__ == "__main__":
     import doctest

     doctest.testmod()

from pathlib import Path
from typing import List

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText


if is_torch_available():
     import torch

if is_vision_available():
     from PIL import Image


_SCREAMING_SNAKE_CASE 		=      ["""text""", """image""", """audio"""]





def 			lowercase(	UpperCamelCase_       )		->	Optional[Any]:







 '''simple docstring'''
 UpperCamelCase			  =      []

 for input_type in input_types:
  if input_type == "text":
   inputs.append("""Text input"""       )
  elif input_type == "image":
   inputs.append(
       Image.open(Path(get_tests_dir("""fixtures/tests_samples/COCO"""       )       ) / """000000039769.png"""       ).resize((512, 512)       )       )
  elif input_type == "audio":
   inputs.append(torch.ones(3000       )       )
  elif isinstance(UpperCamelCase_							,					UpperCamelCase_       ):
   inputs.append(create_inputs(UpperCamelCase_       )       )
  else:
   raise ValueError(f"""Invalid type requested: {input_type}"""       )

 return inputs





def 			lowercase(	UpperCamelCase_       )		->	Tuple:







 '''simple docstring'''
 UpperCamelCase			  =      []

 for output in outputs:
  if isinstance(UpperCamelCase_							,					(str, AgentText)       ):
   output_types.append("""text"""       )
  elif isinstance(UpperCamelCase_							,					(Image.Image, AgentImage)       ):
   output_types.append("""image"""       )
  elif isinstance(UpperCamelCase_							,					(torch.Tensor, AgentAudio)       ):
   output_types.append("""audio"""       )
  else:
   raise ValueError(f"""Invalid output: {output}"""       )

 return output_types


@is_tool_test
class SCREAMING_SNAKE_CASE_		:







   def 						lowerCamelCase_	(     self						:						int				):





    """simple docstring"""


    self.assertTrue(hasattr(self.tool   ,   """inputs"""				)				)
    self.assertTrue(hasattr(self.tool   ,   """outputs"""				)				)

    UpperCamelCase			  =      self.tool.inputs
    for _input in inputs:
     if isinstance(_input   ,   lowerCamelCase_				):
      for __input in _input:
       self.assertTrue(__input in authorized_types				)
     else:
      self.assertTrue(_input in authorized_types				)

    UpperCamelCase			  =      self.tool.outputs
    for _output in outputs:
     self.assertTrue(_output in authorized_types				)







   def 						lowerCamelCase_	(     self						:						List[Any]				):





    """simple docstring"""


    UpperCamelCase			  =      create_inputs(self.tool.inputs				)
    UpperCamelCase			  =      self.tool(*lowerCamelCase_				)

    # There is a single output
    if len(self.tool.outputs				) == 1:
     UpperCamelCase			  =      [outputs]

    self.assertListEqual(output_types(lowerCamelCase_				)   ,   self.tool.outputs				)







   def 						lowerCamelCase_	(     self						:						Union[str, Any]				):





    """simple docstring"""


    self.assertTrue(hasattr(self.tool   ,   """description"""				)				)
    self.assertTrue(hasattr(self.tool   ,   """default_checkpoint"""				)				)
    self.assertTrue(self.tool.description.startswith("""This is a tool that"""				)				)







   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    UpperCamelCase			  =      create_inputs(self.tool.inputs				)
    UpperCamelCase			  =      self.tool(*lowerCamelCase_				)

    if not isinstance(lowerCamelCase_   ,   lowerCamelCase_				):
     UpperCamelCase			  =      [outputs]

    self.assertEqual(len(lowerCamelCase_				)   ,   len(self.tool.outputs				)				)

    for output, output_type in zip(lowerCamelCase_   ,   self.tool.outputs				):
     UpperCamelCase			  =      AGENT_TYPE_MAPPING[output_type]
     self.assertTrue(isinstance(lowerCamelCase_   ,   lowerCamelCase_				)				)







   def 						lowerCamelCase_	(     self						:						Optional[int]				):





    """simple docstring"""


    UpperCamelCase			  =      create_inputs(self.tool.inputs				)

    UpperCamelCase			  =      []

    for _input, input_type in zip(lowerCamelCase_   ,   self.tool.inputs				):
     if isinstance(lowerCamelCase_   ,   lowerCamelCase_				):
      _inputs.append([AGENT_TYPE_MAPPING[_input_type](_input				) for _input_type in input_type]				)
     else:
      _inputs.append(AGENT_TYPE_MAPPING[input_type](_input				)				)

        # Should not raise an error
    UpperCamelCase			  =      self.tool(*lowerCamelCase_				)

    if not isinstance(lowerCamelCase_   ,   lowerCamelCase_				):
     UpperCamelCase			  =      [outputs]

    self.assertEqual(len(lowerCamelCase_				)   ,   len(self.tool.outputs				)				)
